Hybrid feedforward ANN with NLS-based regression curve fitting for US air traffic forecasting
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ORIGINAL ARTICLE
Hybrid feedforward ANN with NLS-based regression curve fitting for US air traffic forecasting Foued Saaˆdaoui1
•
Hayet Saadaoui2,3 • Hana Rabbouch2,4
Received: 28 February 2019 / Accepted: 3 October 2019 Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Due to the rapid growth of the number of passengers over the few recent decades, air traffic forecasting has become a crucial tool for digital transportation systems, playing a fundamental role in the planning and development of traffic management and control systems. The main goal of forecasting in air transport is to predict traffic conditions in a network, on the basis of its past behavior, in order to improve safety and reduce airspace congestion. Nevertheless, air traffic time series often present an intricate behavior because of their irregular trends and strong seasonalities. In this paper, the methodology based on time series decomposition and artificial neural networks (ANNs) is thus reviewed and reconsidered within this framework of air traffic management. In this respect, a hybrid approach coupling feedforward neural networks with a nonlinear least squares-based regression curve fitting is developed for the multistep-ahead prediction. Empirical experiments are conducted in order to demonstrate the effectiveness of the proposed model on passenger traffic real datasets. The results show that, despite its simplicity, the base model is capable of generating accurate forecasts, with a performance comparable with that of powerful state-of-the-art forecasting models. In addition, there is evidence that trend pretreatment (wholly or partially) would rather degrade the forecasting accuracy. Keywords Air traffic management Artificial neural networks Hybrid strategies Feedforward models Forecasting
1 Introduction
& Foued Saaˆdaoui [email protected] Hayet Saadaoui [email protected] Hana Rabbouch [email protected] 1
Department of Statistics, Faculty of Sciences, King Abdulaziz University, PO BOX 80203, Jeddah 21589, Saudi Arabia
2
Laboratoire d’Alge`bre, The´orie de Nombres et Analyse Nonline´aire, Faculte´ des Sciences, University of Monastir, 5019 Monastir, Tunisia
3
LAMETA, INRA, 2 Place Viala, Baˆtiment 26, 34060 Montpellier, France
4
Institut Supe´rieur de Gestion de Tunis, Universite´ de Tunis, Cite´ Bouchoucha, 2000 Tunis, Tunisia
Nowadays, the air traffic has become one of the most significant types of transport for people. Travelers prefer this type of transport because it is rapid, cheap, and accessible for all. Over the seven last decades, the air transport industry has reached an average annual growth rate of almost 10%. In the USA, air traffic is expected to increase twofold within the next few years, which may create significant congestion problems under the current air traffic practices [34]. Moreover, it has been proven that the growth of aviation activity is closely related to developments in the overall US economy. The stati
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